Additional post hoc statistical analyses
# Model with z-scored responses (all three groups, MS, NMS and US)
# z.score(dataExp2, "workerId", "enteredResponse") -> dataZscored
# dataZscored$group <- relevel(dataZscored$group, ref="NMS")
# modelZscored <- lmer(z.enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)||workerId) + (1+group||word), control=lmerControl(optimizer="bobyqa"), dataZscored)
# saveRDS(modelZscored, file = "modelZscored.rds")
modelZscored <- readRDS("./modelZscored.rds")
kable(xtable(summary(modelZscored)$coef), digits=3, caption="Modeling z-scored well-formedness ratings with a linear mixed-effects model")
Modeling z-scored well-formedness ratings with a linear mixed-effects model
| (Intercept) |
-0.001 |
0.012 |
1741.838 |
-0.063 |
0.950 |
| c.(scoreDict) |
0.497 |
0.054 |
566.506 |
9.279 |
0.000 |
| groupMS |
0.001 |
0.012 |
1695.669 |
0.093 |
0.926 |
| groupUS |
0.000 |
0.012 |
1735.778 |
-0.027 |
0.979 |
| c.(scoreRs) |
0.108 |
0.023 |
1333.028 |
4.772 |
0.000 |
| c.(scoreUnseg) |
-0.120 |
0.020 |
1041.082 |
-6.015 |
0.000 |
| c.(scoreDict):groupMS |
0.056 |
0.084 |
253.532 |
0.670 |
0.503 |
| c.(scoreDict):groupUS |
-0.561 |
0.069 |
337.345 |
-8.157 |
0.000 |
| groupMS:c.(scoreRs) |
0.008 |
0.025 |
333.057 |
0.330 |
0.742 |
| groupUS:c.(scoreRs) |
-0.063 |
0.024 |
710.311 |
-2.612 |
0.009 |
| groupMS:c.(scoreUnseg) |
0.043 |
0.024 |
250.885 |
1.829 |
0.069 |
| groupUS:c.(scoreUnseg) |
0.100 |
0.022 |
497.162 |
4.550 |
0.000 |
# The following predictors are added one by one to the linear mixed-effects model presented in Fig.2.
# (a) Phonotactics derived from the dictionary of Māori words in New Zealand English (Macalister, 2005)
# modelMāoriBorrowings <- lmer(enteredResponse ~ c.(scoreDictNze)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreDictNze) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelMāoriBorrowings, file = "modelMāoriBorrowings.rds")
modelMāoriBorrowings <- readRDS("./modelMāoriBorrowings.rds")
kable(xtable(summary(modelMāoriBorrowings)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics derived from the dictionary of Māori words in New Zealand English")
Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics derived from the dictionary of Māori words in New Zealand English
| (Intercept) |
2.798 |
0.052 |
279.213 |
53.627 |
0.000 |
| c.(scoreDictNze) |
0.434 |
0.162 |
1356.624 |
2.678 |
0.008 |
| groupMS |
0.005 |
0.099 |
246.335 |
0.047 |
0.962 |
| groupUS |
0.326 |
0.076 |
256.062 |
4.311 |
0.000 |
| c.(scoreDict) |
0.483 |
0.094 |
742.091 |
5.155 |
0.000 |
| c.(scoreRs) |
0.132 |
0.029 |
1303.639 |
4.542 |
0.000 |
| c.(scoreUnseg) |
-0.157 |
0.026 |
966.223 |
-6.093 |
0.000 |
| c.(scoreDictNze):groupMS |
-0.169 |
0.176 |
324.977 |
-0.963 |
0.336 |
| c.(scoreDictNze):groupUS |
-0.240 |
0.173 |
727.496 |
-1.382 |
0.167 |
| groupMS:c.(scoreDict) |
0.088 |
0.135 |
255.346 |
0.649 |
0.517 |
| groupUS:c.(scoreDict) |
-0.597 |
0.115 |
391.737 |
-5.193 |
0.000 |
| groupMS:c.(scoreRs) |
0.005 |
0.032 |
307.499 |
0.142 |
0.887 |
| groupUS:c.(scoreRs) |
-0.084 |
0.031 |
679.990 |
-2.686 |
0.007 |
| groupMS:c.(scoreUnseg) |
0.061 |
0.031 |
234.060 |
1.950 |
0.052 |
| groupUS:c.(scoreUnseg) |
0.130 |
0.029 |
460.185 |
4.476 |
0.000 |
# (b) Phonotactics derived from NMS’ small active lexicon of Māori (comprising of 121 common loanwords that most NMS can identify as Māori words in our previous study plus 55 urban placenames)
# modelNMSLexicon <- lmer(enteredResponse ~ c.(scoreSmall)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreSmall) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelNMSLexicon, file = "modelNMSLexicon.rds")
modelNMSLexicon <- readRDS("./modelNMSLexicon.rds")
kable(xtable(summary(modelNMSLexicon)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics derived from NMS’ small active lexicon of Māori")
Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics derived from NMS’ small active lexicon of Māori
| (Intercept) |
2.798 |
0.052 |
279.286 |
53.636 |
0.000 |
| c.(scoreSmall) |
0.436 |
0.235 |
1411.175 |
1.858 |
0.063 |
| groupMS |
0.005 |
0.099 |
246.333 |
0.055 |
0.956 |
| groupUS |
0.327 |
0.076 |
256.037 |
4.318 |
0.000 |
| c.(scoreDict) |
0.587 |
0.076 |
581.840 |
7.699 |
0.000 |
| c.(scoreRs) |
0.136 |
0.029 |
1298.057 |
4.674 |
0.000 |
| c.(scoreUnseg) |
-0.160 |
0.026 |
963.925 |
-6.245 |
0.000 |
| c.(scoreSmall):groupMS |
-0.352 |
0.244 |
346.084 |
-1.444 |
0.150 |
| c.(scoreSmall):groupUS |
-0.362 |
0.247 |
801.583 |
-1.469 |
0.142 |
| groupMS:c.(scoreDict) |
0.066 |
0.118 |
245.848 |
0.563 |
0.574 |
| groupUS:c.(scoreDict) |
-0.641 |
0.097 |
338.684 |
-6.577 |
0.000 |
| groupMS:c.(scoreRs) |
0.007 |
0.032 |
305.144 |
0.217 |
0.828 |
| groupUS:c.(scoreRs) |
-0.085 |
0.031 |
672.445 |
-2.694 |
0.007 |
| groupMS:c.(scoreUnseg) |
0.061 |
0.031 |
232.214 |
1.967 |
0.050 |
| groupUS:c.(scoreUnseg) |
0.132 |
0.029 |
457.164 |
4.556 |
0.000 |
# (c) Phonotactics generalized over a list of function words
# modelFunction <- lmer(enteredResponse ~ c.(scoreFunc)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreFunc) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelFunction, file = "modelFunction.rds")
modelFunction <- readRDS("./modelFunction.rds")
kable(xtable(summary(modelFunction)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics generalized over a list of function words")
Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics generalized over a list of function words
| (Intercept) |
2.798 |
0.052 |
279.340 |
53.651 |
0.000 |
| c.(scoreFunc) |
0.271 |
0.165 |
1377.414 |
1.642 |
0.101 |
| groupMS |
0.005 |
0.099 |
246.359 |
0.053 |
0.958 |
| groupUS |
0.327 |
0.076 |
256.071 |
4.314 |
0.000 |
| c.(scoreDict) |
0.629 |
0.069 |
526.485 |
9.073 |
0.000 |
| c.(scoreRs) |
0.134 |
0.029 |
1312.144 |
4.567 |
0.000 |
| c.(scoreUnseg) |
-0.163 |
0.026 |
969.464 |
-6.363 |
0.000 |
| c.(scoreFunc):groupMS |
-0.010 |
0.171 |
322.338 |
-0.059 |
0.953 |
| c.(scoreFunc):groupUS |
-0.035 |
0.174 |
764.269 |
-0.200 |
0.842 |
| groupMS:c.(scoreDict) |
0.026 |
0.110 |
241.969 |
0.235 |
0.814 |
| groupUS:c.(scoreDict) |
-0.681 |
0.090 |
320.894 |
-7.573 |
0.000 |
| groupMS:c.(scoreRs) |
0.001 |
0.032 |
307.824 |
0.021 |
0.983 |
| groupUS:c.(scoreRs) |
-0.089 |
0.031 |
686.135 |
-2.842 |
0.005 |
| groupMS:c.(scoreUnseg) |
0.062 |
0.031 |
233.516 |
1.975 |
0.049 |
| groupUS:c.(scoreUnseg) |
0.131 |
0.029 |
460.344 |
4.547 |
0.000 |
# (d) English phonotactics obtained from the English lexical database CELEX (Baayen, Piepen-brock & van H, 1993)
# modelEng <- lmer(enteredResponse ~ c.(scoreEng)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreEng) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelEng, file = "modelEng.rds")
modelEng <- readRDS("./modelEng.rds")
kable(xtable(summary(modelEng)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the English phonotactics obtained from the English lexical database CELEX")
Modeling well-formedness ratings with a linear mixed-effects model including the English phonotactics obtained from the English lexical database CELEX
| (Intercept) |
2.798 |
0.052 |
279.320 |
53.573 |
0.000 |
| c.(scoreEng) |
-0.032 |
0.019 |
1319.566 |
-1.751 |
0.080 |
| groupMS |
0.006 |
0.099 |
246.371 |
0.060 |
0.952 |
| groupUS |
0.328 |
0.076 |
256.012 |
4.333 |
0.000 |
| c.(scoreDict) |
0.641 |
0.069 |
530.596 |
9.339 |
0.000 |
| c.(scoreRs) |
0.143 |
0.029 |
1304.275 |
4.976 |
0.000 |
| c.(scoreUnseg) |
-0.161 |
0.026 |
962.520 |
-6.253 |
0.000 |
| c.(scoreEng):groupMS |
0.005 |
0.021 |
319.815 |
0.232 |
0.816 |
| c.(scoreEng):groupUS |
-0.017 |
0.020 |
697.304 |
-0.848 |
0.397 |
| groupMS:c.(scoreDict) |
0.024 |
0.109 |
243.111 |
0.218 |
0.828 |
| groupUS:c.(scoreDict) |
-0.683 |
0.089 |
322.407 |
-7.681 |
0.000 |
| groupMS:c.(scoreRs) |
0.000 |
0.032 |
307.677 |
-0.012 |
0.990 |
| groupUS:c.(scoreRs) |
-0.093 |
0.031 |
675.526 |
-3.006 |
0.003 |
| groupMS:c.(scoreUnseg) |
0.062 |
0.031 |
232.117 |
1.986 |
0.048 |
| groupUS:c.(scoreUnseg) |
0.133 |
0.029 |
455.149 |
4.613 |
0.000 |
# (e) Word shape score obtained from a trigram language model built by identifying each segment as a consonant, vowel or long vowel and calculating probabilities over sequences of those categories
# modelWordshape <- lmer(enteredResponse ~ c.(scoreCV)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scoreCV) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelWordshape, file = "modelWordshape.rds")
modelWordshape <- readRDS("./modelWordshape.rds")
kable(xtable(summary(modelWordshape)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including word shape scores")
Modeling well-formedness ratings with a linear mixed-effects model including word shape scores
| (Intercept) |
2.785 |
0.053 |
280.210 |
52.977 |
0.000 |
| c.(scoreCV) |
-0.019 |
0.026 |
555.046 |
-0.713 |
0.476 |
| groupMS |
-0.011 |
0.100 |
246.347 |
-0.115 |
0.908 |
| groupUS |
0.323 |
0.076 |
255.726 |
4.232 |
0.000 |
| c.(scoreDict) |
0.671 |
0.070 |
973.649 |
9.629 |
0.000 |
| c.(scoreRs) |
0.139 |
0.029 |
1300.170 |
4.841 |
0.000 |
| c.(scoreUnseg) |
-0.159 |
0.026 |
1005.330 |
-6.169 |
0.000 |
| c.(scoreCV):groupMS |
-0.038 |
0.042 |
253.720 |
-0.920 |
0.358 |
| c.(scoreCV):groupUS |
-0.059 |
0.034 |
333.169 |
-1.748 |
0.081 |
| groupMS:c.(scoreDict) |
0.106 |
0.092 |
271.120 |
1.161 |
0.247 |
| groupUS:c.(scoreDict) |
-0.545 |
0.081 |
464.426 |
-6.753 |
0.000 |
| groupMS:c.(scoreRs) |
-0.002 |
0.032 |
305.368 |
-0.077 |
0.939 |
| groupUS:c.(scoreRs) |
-0.094 |
0.031 |
650.843 |
-3.080 |
0.002 |
| groupMS:c.(scoreUnseg) |
0.052 |
0.030 |
234.587 |
1.722 |
0.086 |
| groupUS:c.(scoreUnseg) |
0.105 |
0.028 |
465.731 |
3.718 |
0.000 |
# (f) Position-length specific probabilities of phonemes obtained from a unigram language model
# modelPosLength <- lmer(enteredResponse ~ c.(scorePosLength)*group + c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + (1 + c.(scorePosLength) + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelPosLength, file = "modelPosLength.rds")
modelPosLength <- readRDS("./modelPosLength.rds")
kable(xtable(summary(modelPosLength)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including position-length specific probabilities of phonemes")
Modeling well-formedness ratings with a linear mixed-effects model including position-length specific probabilities of phonemes
| (Intercept) |
2.809 |
0.054 |
276.178 |
52.130 |
0.000 |
| c.(scorePosLength) |
-0.073 |
0.080 |
482.355 |
-0.912 |
0.362 |
| groupMS |
0.021 |
0.102 |
245.116 |
0.202 |
0.840 |
| groupUS |
0.330 |
0.078 |
254.322 |
4.211 |
0.000 |
| c.(scoreDict) |
0.643 |
0.071 |
689.026 |
9.065 |
0.000 |
| c.(scoreRs) |
0.141 |
0.029 |
1260.914 |
4.936 |
0.000 |
| c.(scoreUnseg) |
-0.153 |
0.025 |
966.605 |
-6.100 |
0.000 |
| c.(scorePosLength):groupMS |
-0.136 |
0.122 |
201.490 |
-1.118 |
0.265 |
| c.(scorePosLength):groupUS |
-0.076 |
0.101 |
277.885 |
-0.758 |
0.449 |
| groupMS:c.(scoreDict) |
0.100 |
0.104 |
252.568 |
0.960 |
0.338 |
| groupUS:c.(scoreDict) |
-0.624 |
0.088 |
374.400 |
-7.088 |
0.000 |
| groupMS:c.(scoreRs) |
-0.005 |
0.032 |
296.740 |
-0.170 |
0.865 |
| groupUS:c.(scoreRs) |
-0.095 |
0.031 |
648.074 |
-3.071 |
0.002 |
| groupMS:c.(scoreUnseg) |
0.054 |
0.030 |
226.573 |
1.788 |
0.075 |
| groupUS:c.(scoreUnseg) |
0.122 |
0.028 |
456.681 |
4.323 |
0.000 |
# (g) Presence of macron(s)
# dataExp2$macron <- FALSE
# dataExp2[grepl("ā|ē|ī|ō|ū",dataExp2$word),]$macron <- TRUE
# modelMacron <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + macron*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + macron|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelMacron, file = "modelMacron.rds")
modelMacron <- readRDS("./modelMacron.rds")
kable(xtable(summary(modelMacron)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the presence of macron(s)")
Modeling well-formedness ratings with a linear mixed-effects model including the presence of macron(s)
| (Intercept) |
2.365 |
0.116 |
274.615 |
20.403 |
0.000 |
| c.(scoreDict) |
0.836 |
0.093 |
279.283 |
8.995 |
0.000 |
| groupNMS |
-0.202 |
0.133 |
258.315 |
-1.525 |
0.128 |
| groupUS |
0.216 |
0.137 |
262.651 |
1.583 |
0.115 |
| c.(scoreRs) |
0.120 |
0.029 |
412.034 |
4.087 |
0.000 |
| c.(scoreUnseg) |
-0.078 |
0.028 |
293.376 |
-2.734 |
0.007 |
| macronTRUE |
0.511 |
0.104 |
289.422 |
4.906 |
0.000 |
| c.(scoreDict):groupNMS |
0.033 |
0.105 |
252.406 |
0.313 |
0.754 |
| c.(scoreDict):groupUS |
-0.676 |
0.109 |
258.916 |
-6.213 |
0.000 |
| groupNMS:c.(scoreRs) |
-0.007 |
0.031 |
307.677 |
-0.210 |
0.834 |
| groupUS:c.(scoreRs) |
-0.094 |
0.033 |
332.431 |
-2.831 |
0.005 |
| groupNMS:c.(scoreUnseg) |
-0.056 |
0.031 |
233.392 |
-1.808 |
0.072 |
| groupUS:c.(scoreUnseg) |
0.068 |
0.032 |
248.470 |
2.104 |
0.036 |
| groupNMS:macronTRUE |
0.212 |
0.118 |
264.864 |
1.788 |
0.075 |
| groupUS:macronTRUE |
0.110 |
0.122 |
271.421 |
0.902 |
0.368 |
# (h) Presence of digraph(s) such as wh and ng
dataExp2$digraph <- FALSE
dataExp2[grepl("wh|ng",dataExp2$word),]$digraph <- TRUE
# modelDigraph <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + digraph*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + digraph|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelDigraph, file = "modelDigraph.rds")
modelDigraph <- readRDS("./modelDigraph.rds")
kable(xtable(summary(modelDigraph)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the presence of digraph(s)")
Modeling well-formedness ratings with a linear mixed-effects model including the presence of digraph(s)
| (Intercept) |
2.768 |
0.087 |
252.808 |
31.895 |
0.000 |
| c.(scoreDict) |
0.667 |
0.097 |
274.197 |
6.895 |
0.000 |
| groupNMS |
-0.032 |
0.100 |
247.124 |
-0.319 |
0.750 |
| groupUS |
0.403 |
0.103 |
248.074 |
3.906 |
0.000 |
| c.(scoreRs) |
0.153 |
0.030 |
441.603 |
5.171 |
0.000 |
| c.(scoreUnseg) |
-0.084 |
0.029 |
304.051 |
-2.934 |
0.004 |
| digraphTRUE |
0.099 |
0.055 |
286.562 |
1.811 |
0.071 |
| c.(scoreDict):groupNMS |
-0.024 |
0.109 |
242.709 |
-0.224 |
0.823 |
| c.(scoreDict):groupUS |
-0.711 |
0.113 |
248.382 |
-6.311 |
0.000 |
| groupNMS:c.(scoreRs) |
0.001 |
0.030 |
289.424 |
0.026 |
0.980 |
| groupUS:c.(scoreRs) |
-0.106 |
0.032 |
312.824 |
-3.305 |
0.001 |
| groupNMS:c.(scoreUnseg) |
-0.053 |
0.031 |
218.472 |
-1.752 |
0.081 |
| groupUS:c.(scoreUnseg) |
0.036 |
0.032 |
233.309 |
1.130 |
0.260 |
| groupNMS:digraphTRUE |
0.076 |
0.061 |
238.137 |
1.263 |
0.208 |
| groupUS:digraphTRUE |
-0.238 |
0.063 |
246.301 |
-3.789 |
0.000 |
# (i) Maximum length of vowel sequence
# Add the maximum length of vowel sequence
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū][aeiouāēīōū][aeiouāēīōū][aeiouāēīōū]","5", dataExp2$word)
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū][aeiouāēīōū][aeiouāēīōū]","4", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū][aeiouāēīōū]","3", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[aeiouāēīōū][aeiouāēīōū]","2", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[aeiouāēīōū]","1", dataExp2$vSeq)
dataExp2$vSeq <- gsub("[whkmrngpt]","", dataExp2$vSeq)
dataExp2$vSeq <- gsub(""," ",dataExp2$vSeq)
vSeqList <- strsplit(dataExp2$vSeq,split=" ");vSeqList1 <- sapply(vSeqList,max)
dataExp2$vSeq <- vSeqList1;dataExp2$vSeq <- as.numeric(dataExp2$vSeq)
# modelVSeq <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + c.(vSeq)*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + c.(vSeq)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelVSeq, file = "modelVSeq.rds")
modelVSeq <- readRDS("./modelVSeq.rds")
kable(xtable(summary(modelVSeq)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the maximum length of vowel sequence")
Modeling well-formedness ratings with a linear mixed-effects model including the maximum length of vowel sequence
| (Intercept) |
2.802 |
0.084 |
243.225 |
33.364 |
0.000 |
| c.(scoreDict) |
0.601 |
0.089 |
228.609 |
6.771 |
0.000 |
| groupNMS |
-0.004 |
0.099 |
251.108 |
-0.041 |
0.967 |
| groupUS |
0.323 |
0.100 |
245.277 |
3.218 |
0.001 |
| c.(scoreRs) |
0.137 |
0.023 |
230.338 |
5.851 |
0.000 |
| c.(scoreUnseg) |
-0.066 |
0.020 |
174.820 |
-3.354 |
0.001 |
| c.(vSeq) |
-0.144 |
0.037 |
231.709 |
-3.859 |
0.000 |
| c.(scoreDict):groupNMS |
-0.131 |
0.110 |
296.457 |
-1.189 |
0.235 |
| c.(scoreDict):groupUS |
-0.688 |
0.108 |
246.206 |
-6.374 |
0.000 |
| groupNMS:c.(scoreRs) |
-0.024 |
0.035 |
559.126 |
-0.691 |
0.490 |
| groupUS:c.(scoreRs) |
-0.089 |
0.030 |
309.903 |
-2.941 |
0.004 |
| groupNMS:c.(scoreUnseg) |
-0.010 |
0.030 |
443.430 |
-0.317 |
0.752 |
| groupUS:c.(scoreUnseg) |
0.058 |
0.026 |
238.732 |
2.260 |
0.025 |
| groupNMS:c.(vSeq) |
-0.179 |
0.047 |
304.119 |
-3.825 |
0.000 |
| groupUS:c.(vSeq) |
0.061 |
0.046 |
249.698 |
1.336 |
0.183 |
# (j) Type of first segment (consonant or vowel)
dataExp2$first <- gsub("[aeiouāēīōū]","v",dataExp2$word)
dataExp2$first <- gsub("[whkmrngpt]","c",dataExp2$first)
dataExp2$first <- substr(dataExp2$first,1,1)
dataExp2$first <- as.factor(dataExp2$first)
# modelFirst <- lmer(enteredResponse ~ c.(scoreDict)*group + c.(scoreRs)*group + c.(scoreUnseg)*group + first*group + (1 + c.(scoreDict) + c.(scoreRs) + c.(scoreUnseg) + first|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelFirst, file = "modelFirst.rds")
modelFirst <- readRDS("./modelFirst.rds")
kable(xtable(summary(modelFirst)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the type of first segment")
Modeling well-formedness ratings with a linear mixed-effects model including the type of first segment
| (Intercept) |
2.951 |
0.089 |
243.507 |
33.243 |
0.000 |
| c.(scoreDict) |
0.631 |
0.088 |
229.620 |
7.133 |
0.000 |
| groupNMS |
0.066 |
0.105 |
262.603 |
0.628 |
0.531 |
| groupUS |
0.211 |
0.107 |
248.303 |
1.984 |
0.048 |
| c.(scoreRs) |
0.125 |
0.024 |
228.684 |
5.131 |
0.000 |
| c.(scoreUnseg) |
-0.080 |
0.023 |
188.820 |
-3.535 |
0.001 |
| firstv |
-0.227 |
0.057 |
221.276 |
-4.015 |
0.000 |
| c.(scoreDict):groupNMS |
-0.112 |
0.110 |
302.658 |
-1.009 |
0.314 |
| c.(scoreDict):groupUS |
-0.683 |
0.108 |
247.854 |
-6.341 |
0.000 |
| groupNMS:c.(scoreRs) |
-0.017 |
0.037 |
581.094 |
-0.462 |
0.644 |
| groupUS:c.(scoreRs) |
-0.076 |
0.032 |
306.086 |
-2.425 |
0.016 |
| groupNMS:c.(scoreUnseg) |
-0.022 |
0.033 |
427.409 |
-0.676 |
0.499 |
| groupUS:c.(scoreUnseg) |
0.055 |
0.029 |
242.159 |
1.914 |
0.057 |
| groupNMS:firstv |
-0.155 |
0.071 |
302.046 |
-2.172 |
0.031 |
| groupUS:firstv |
0.169 |
0.069 |
240.424 |
2.459 |
0.015 |
# (k) New phonotactic scores
# modelNewScores <- lmer(enteredResponse ~ c.(scoreDictNew)*group + c.(scoreRsNew)*group + c.(scoreUnsegNew)*group + (1 + c.(scoreDictNew) + c.(scoreRsNew) + c.(scoreUnsegNew)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelNewScores, file = "modelNewScores.rds")
modelNewScores <- readRDS("./modelNewScores.rds")
kable(xtable(summary(modelNewScores)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics....")
Modeling well-formedness ratings with a linear mixed-effects model including the phonotactics….
| (Intercept) |
2.780 |
0.086 |
248.976 |
32.363 |
0.000 |
| c.(scoreDictNew) |
0.807 |
0.101 |
287.717 |
7.970 |
0.000 |
| groupNMS |
0.007 |
0.100 |
245.713 |
0.068 |
0.946 |
| groupUS |
0.328 |
0.102 |
246.891 |
3.204 |
0.002 |
| c.(scoreRsNew) |
0.214 |
0.041 |
441.975 |
5.242 |
0.000 |
| c.(scoreUnsegNew) |
-0.178 |
0.038 |
305.371 |
-4.623 |
0.000 |
| c.(scoreDictNew):groupNMS |
0.002 |
0.113 |
245.740 |
0.018 |
0.986 |
| c.(scoreDictNew):groupUS |
-0.707 |
0.118 |
259.167 |
-6.007 |
0.000 |
| groupNMS:c.(scoreRsNew) |
0.071 |
0.043 |
316.982 |
1.650 |
0.100 |
| groupUS:c.(scoreRsNew) |
-0.169 |
0.046 |
356.124 |
-3.699 |
0.000 |
| groupNMS:c.(scoreUnsegNew) |
-0.089 |
0.042 |
246.341 |
-2.117 |
0.035 |
| groupUS:c.(scoreUnsegNew) |
0.073 |
0.044 |
265.513 |
1.642 |
0.102 |
# (l) Phonotactic scores without the distinction between short and long vowels marked with macrons
# modelNoMacron <- lmer(enteredResponse ~ c.(scoreDictNoMacron)*group + c.(scoreRsNoMacron)*group + c.(scoreUnsegNoMacron)*group + (1 + c.(scoreDictNoMacron) + c.(scoreRsNoMacron) + c.(scoreUnsegNoMacron)|workerId) + (1+group|word), control=lmerControl(optimizer="bobyqa"), dataExp2)
# saveRDS(modelNoMacron, file = "modelNoMacron.rds")
modelNoMacron <- readRDS("./modelNoMacron.rds")
kable(xtable(summary(modelNoMacron)$coef), digits=3, caption="Modeling well-formedness ratings with a linear mixed-effects model with phonotactic scores without the distinction between short and long vowels marked with macrons")
Modeling well-formedness ratings with a linear mixed-effects model with phonotactic scores without the distinction between short and long vowels marked with macrons
| (Intercept) |
2.798 |
0.085 |
243.378 |
33.013 |
0.000 |
| c.(scoreDictNoMacron) |
1.028 |
0.105 |
224.650 |
9.821 |
0.000 |
| groupNMS |
-0.002 |
0.099 |
249.863 |
-0.016 |
0.988 |
| groupUS |
0.327 |
0.101 |
245.283 |
3.227 |
0.001 |
| c.(scoreRsNoMacron) |
0.124 |
0.037 |
225.738 |
3.341 |
0.001 |
| c.(scoreUnsegNoMacron) |
-0.267 |
0.049 |
209.213 |
-5.483 |
0.000 |
| c.(scoreDictNoMacron):groupNMS |
0.155 |
0.133 |
316.631 |
1.170 |
0.243 |
| c.(scoreDictNoMacron):groupUS |
-0.748 |
0.128 |
250.443 |
-5.822 |
0.000 |
| groupNMS:c.(scoreRsNoMacron) |
0.004 |
0.058 |
624.395 |
0.067 |
0.946 |
| groupUS:c.(scoreRsNoMacron) |
-0.083 |
0.049 |
329.315 |
-1.671 |
0.096 |
| groupNMS:c.(scoreUnsegNoMacron) |
-0.045 |
0.062 |
304.241 |
-0.727 |
0.468 |
| groupUS:c.(scoreUnsegNoMacron) |
0.269 |
0.060 |
236.468 |
4.473 |
0.000 |